.png){kind=icon}
返回
Meta-Learning for Multi-Family Android Malware Classification
.png){kind=icon}
OA
AI
DOI：10.1145/3664806.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious applications that exploit unknown vulnerabilities) classification problem. In this article, we introduce an innovative meta-learning approach for multi-family Android malware classification named Meta-MAMC, which uses meta-learning technology to learn meta-knowledge (i.e., the similarities and differences among different malware families) of few-family samples and combines new sampling algorithms to solve the above challenges. Meta-MAMC integrates (i) the meta-knowledge contained within the dataset to guide models in learning to identify unknown malware; and (ii) more accurate and diverse tasks based on novel sampling strategies, as well as directly adapting metalearning to a new few-sample and zero-sample task to classify families. We have evaluated Meta-MAMC on two popular datasets and a corpus of real-world Android applications. The results demonstrate its efficacy in accurately classifying malicious applications belonging to certain malware families, even achieving 100% classification in some families.
Keyword:
Android
malware family
meta-learning
classification
期刊
A
IF:
6.2
论文数:
1.2K
被引数:
3.4K
机构
引用论文
Anastomotic Leakage Using Linear Stapling Device with Pre-attached Bioabsorbable Polyglycolic Acid Felt After Laparoscopic Anterior Resection腹腔镜前切除术后使用带有预附着生物可吸收聚乙醇酸毡的线性吻合装置的吻合口漏OAAI
OAAI分享收藏Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis基于频繁子图分析的Android恶意软件家族分类及代表性样本选择
PREAI
分享收藏分享收藏A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容